{
  "id": 17742599,
  "title": "Prune Gradually, Adapt Sparsely: Efficient Pruning of Large Language Models at Extreme Sparsity",
  "url": "https://urgent.news/2026/10/30/prune-gradually-adapt-sparsely-efficient-pruning-of-large-language",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-30T16:17:43.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.41968v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Weight pruning can reduce the storage and inference cost of large language models (LLMs), but preserving quality at high sparsity levels remains challenging. Existing methods face a quality-cost trade-off in this regime. One-shot pruning is cheap but degrades sharply as sparsity increases, even when followed by post-pruning recovery. A growing line of work instead optimizes the mask directly from…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}